What does my group do?

What makes them different?

Much human variation is due to difference in ~6 million DNA base pairs (0.1% of genome)

What makes them different?

Genes are expressed differently during different stages and in different tissues.

DNA is packed, making certain parts inaccessible, and this packing is dynamic.

DNA methylation is a chemical modification of DNA, involved in gene expression regulation.

Large blocks of hypo-methylation in colon cancer

• overlaps with other important genomic domains
• genes within these blocks are tissue-specific

Genes with hyper-variable expression in colon cancer are enriched within these blocks.

Hypo-methylation blocks observed across five solid tumor types.

Gene expression hyper-variability enriched in hypo-methylation blocks in other cancer types.

Genes with consistent hyper-variable expression across tumors are tissue-specific.

• large domains of methylation loss are a stable mark across cancer types
• gene expression hyper-variability is enriched within these domains
• hyper-variable genes within these regions are tissue-specific and involved in cellular fate

Genes are expressed differently during different stages and in different tissues.

Collaborative and exploratory analysis

• Data transformation and modeling: data smoothing, region finding (R/Bioconductor: Bsmooth, minfi)
• Exploration: search by gene, search by overlap
• Contextual analysis: overlap with other data (our own, other labs, UCSC, ensembl)

• We have unprecedented ability to measure
• and lots of publicly available data to contextualize it

Integrative, visual and computational exploratory analysis of genomic data

• Browser-based
• Interactive
• Integration of data
• Reproducible dissemination
• Communication with R/Bioconductor: epivizr package

Creativity in exploration

We are building software applications to support creative exploratory analysis of large genome-wide datasets…

Summarization: summarize integrated measurements (computed on data subsets)

Statistically-guided exploration: Calculate a statistic of interest

# Get tumor methylation base-pair data
m <- assay(se)[,"tumor"]

# Compute regions with highest variability across cpgs
region_stat <- calcWindowStat(m, step=25, window=80, stat=rowSds)
s <- region_stat[,"stat"]

Explore data based on statistic

What's around the regions with highest across CpG variability?

# get locations in decreasing order
o <- order(s, decreasing=TRUE)
indices <- region_stat[o, "indices"]
slideShowRegions <- rowRanges(se)[indices] + 1250000L
mgr$slideshow(slideShowRegions)

dynamically extensible: Easily integrate new data types and add new visualizations.

• Based on classic "three-table" design in genomic data analysis
• Data providers define coordinate space

Visualization design goals

• Context
• Integrate and align multiple data sources; navigate; search
• Connect: brushing
• Encode: map visualization properties to data on the fly
• Reconfigure: multiple views of the same data

Visualization goals

• Data
• Select and filter: tight-knit integration with R/Bioconductor;
• (current work) filters on visualization propagate to data environment
• Model
• New 'measurements' the result of modeling; perhaps suggested by data context

One interpretation of Big Data is Many relevant sources of contextual data

• Easily access/integrate contextual data
• Driven by exploratory analysis of immediate data
• Iterative process
• Visual and computational exploration go hand in hand

Acknowledgements

Justin Wagner, Jayaram Kancherla (CBCB)
Florin Chelaru (now at Google), Joseph Paulson (now at Genentech)
Feinberg Lab & K. Hansen (JHU), R. Irizarry (Harvard)

Funding: NIH, Genentech, Gates Foundation

More information

http://hcbravo.org
@hcorrada